{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/yangchen/.cache/torch_extensions/marching_cubes/lock\n",
      "/home/yangchen/.cache/torch_extensions/imgproc/lock\n",
      "/home/yangchen/.cache/torch_extensions/indexing/lock\n",
      "/home/yangchen/.cache/torch_extensions/pcproc/lock\n",
      "Jupyter environment detected. Enabling Open3D WebVisualizer.\n",
      "[Open3D INFO] WebRTC GUI backend enabled.\n",
      "[Open3D INFO] WebRTCWindowSystem: HTTP handshake server disabled.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "\n",
    "sys.path.append(\"/home/yangchen/projects/other_experments/di-fusion\")\n",
    "import argparse\n",
    "import logging\n",
    "from collections import defaultdict\n",
    "from pathlib import Path\n",
    "\n",
    "import cv2\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "from utils import exp_util\n",
    "from options import config_parser\n",
    "from dataset.rgbd_dataset import ShapeViewDataset, Voxelizer\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "usage: ipykernel_launcher.py [-h] [--num_view NUM_VIEW] [--reso RESO]\n",
      "                             [--root_path ROOT_PATH] [-v]\n",
      "                             [--max_hits MAX_HITS]\n",
      "                             [--pixel_per_view PIXEL_PER_VIEW]\n",
      "                             [--raymarching_stepsize_ratio RAYMARCHING_STEPSIZE_RATIO]\n",
      "                             [--L1] [--color-weight COLOR_WEIGHT]\n",
      "                             [--depth-weight DEPTH_WEIGHT]\n",
      "                             [--depth-weight-decay DEPTH_WEIGHT_DECAY]\n",
      "                             [--alpha-weight ALPHA_WEIGHT]\n",
      "                             [--vgg-weight VGG_WEIGHT]\n",
      "                             [--eikonal-weight EIKONAL_WEIGHT]\n",
      "                             [--regz-weight REGZ_WEIGHT]\n",
      "                             [--vgg-level {1,2,3,4}] [--eval-lpips]\n",
      "                             [--no-background-loss]\n",
      "ipykernel_launcher.py: error: unrecognized arguments: --ip=127.0.0.1 --stdin=9003 --control=9001 --hb=9000 --Session.signature_scheme=\"hmac-sha256\" --Session.key=b\"8a03baa1-4458-48fd-b59a-da9471d776ae\" --shell=9002 --transport=\"tcp\" --iopub=9004 --f=/tmp/tmp-774691ig7PoqrKMnfM.json\n"
     ]
    },
    {
     "ename": "SystemExit",
     "evalue": "2",
     "output_type": "error",
     "traceback": [
      "An exception has occurred, use %tb to see the full traceback.\n",
      "\u001b[0;31mSystemExit\u001b[0m\u001b[0;31m:\u001b[0m 2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/yangchen/.conda/envs/dif/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3452: UserWarning: To exit: use 'exit', 'quit', or Ctrl-D.\n",
      "  warn(\"To exit: use 'exit', 'quit', or Ctrl-D.\", stacklevel=1)\n"
     ]
    }
   ],
   "source": [
    "logging.basicConfig(level=logging.INFO)\n",
    "parser = config_parser()\n",
    "args = parser.parse_args()\n",
    "logging.info(args)\n",
    "device = torch.device(\"cuda:0\")\n",
    "\n",
    "### init data & model\n",
    "views = 1 ## current we only consider views eqs 1\n",
    "view_ids = [0, 10, 20, 30] ## specify views for recon\n",
    "assert len(view_ids) == args.num_view\n",
    "rgbd_dataset = ShapeViewDataset(args.root_path, views, args.num_view)\n",
    "# trainer = RGBD_NeRF(args).to(device)\n",
    "vox = Voxelizer(args, device)\n",
    "\n",
    "samples = rgbd_dataset.__getitem__(index = 0, view_ids=view_ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "usage: ipykernel_launcher.py [-h]\n",
      "ipykernel_launcher.py: error: unrecognized arguments: --ip=127.0.0.1 --stdin=9003 --control=9001 --hb=9000 --Session.signature_scheme=\"hmac-sha256\" --Session.key=b\"c37ff727-518f-404e-ae50-8c76d0b47bfa\" --shell=9002 --transport=\"tcp\" --iopub=9004 --f=/tmp/tmp-700901gTf5bdQnX4V1.json\n"
     ]
    },
    {
     "ename": "SystemExit",
     "evalue": "2",
     "output_type": "error",
     "traceback": [
      "An exception has occurred, use %tb to see the full traceback.\n",
      "\u001b[0;31mSystemExit\u001b[0m\u001b[0;31m:\u001b[0m 2\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fa17e2f8c90>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "path = \"/data/yangchen/ICL-NUIM/Di-Fusion_demo/depth/0.png\"\n",
    "img = cv2.imread(path, cv2.IMREAD_UNCHANGED)\n",
    "plt.imshow(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(305725,)\n",
      "(307200,)\n"
     ]
    }
   ],
   "source": [
    "print(np.where(img>0.1)[0].shape)\n",
    "print(img.flatten().shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[    0,     0,   745, ...,     0,   100,     0],\n",
       "       [    0,     0,  8165, ..., 11255,    50,     0],\n",
       "       [    0,   300,   145, ...,   195, 11255,     0],\n",
       "       ...,\n",
       "       [    0,     0,     0, ..., 11630, 11630, 11710],\n",
       "       [    0,  1745,  8285, ..., 11630, 11630, 11710],\n",
       "       [ 1195,  8285,  8285, ..., 11710,     0, 11630]], dtype=uint16)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([16])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def homo_pose(pose):\n",
    "    ## use to tranfer pose from 3*4 to 4*4\n",
    "    if isinstance(pose, torch.Tensor):\n",
    "        bottom = torch.tensor([0.,0.,0.,1.]).unsqueeze(0)\n",
    "        pose = torch.cat((pose, bottom), dim=0)\n",
    "    else:\n",
    "        assert isinstance(pose, np.ndarray)\n",
    "        bottom = np.array([0.,0.,0.,1.])[None,:]\n",
    "        pose = np.concatenate((pose, bottom), axis=0)\n",
    "    return pose\n",
    "\n",
    "homo_pose(torch.ones((3,4))).flatten().shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f99109dce10>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = np.load(\"idx.npy\")\n",
    "b = np.arange(a.size)\n",
    "plt.plot(b,a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# here we implement another ssim and psnr metrix\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "120"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base_dir = \"/home/yangchen/projects/other_experments/di-fusion/logs/debug\"\n",
    "import glob\n",
    "files = glob.glob(os.path.join(base_dir, '*.jpg'))\n",
    "\n",
    "paths = sorted(files, key=lambda x:int(x.split(\"_\")[-1][:-4]))[:120]\n",
    "len(paths)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "import skimage\n",
    "def compute_psnr(p, t):\n",
    "    \"\"\"Compute PSNR of model image predictions.\n",
    "    :param prediction: Return value of forward pass.\n",
    "    :param ground_truth: Ground truth.\n",
    "    :return: (psnr, ssim): tuple of floats\n",
    "    \"\"\"\n",
    "    ssim = skimage.metrics.structural_similarity(p, t, multichannel=True, data_range=1)\n",
    "    psnr = skimage.metrics.peak_signal_noise_ratio(p, t, data_range=1)\n",
    "    return ssim, psnr\n",
    "\n",
    "all_ssim=[]\n",
    "all_psnr=[]\n",
    "for i in paths:\n",
    "    assert os.path.isfile(i)\n",
    "    img = cv2.cvtColor(cv2.imread(i), cv2.COLOR_BGR2RGB)\n",
    "\n",
    "    p, t = img[:,:640, :]/255., img[:,640:,:]/255.\n",
    "    ssim, psnr = compute_psnr(p,t)\n",
    "    all_ssim.append(ssim)\n",
    "    all_psnr.append(psnr)\n",
    "\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_array = [0,30,60,90]\n",
    "plt.figure()\n",
    "plt.plot(range(len(all_ssim)), all_ssim)\n",
    "plt.scatter(x=x_array, y=[all_ssim[i] for i in x_array], marker=\"^\", c='r', s=60)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_array = [0,30,60,90]\n",
    "plt.figure()\n",
    "plt.plot(range(len(all_psnr)), all_psnr)\n",
    "plt.scatter(x=x_array, y=[all_psnr[i] for i in x_array], marker=\"^\", c='r', s=60)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "import imageio\n",
    "writer = imageio.get_writer('test.mp4', fps=20)\n",
    "\n",
    "for im in paths:\n",
    "    writer.append_data(imageio.imread(im))\n",
    "writer.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import torch\n",
    "from torchvision import transforms\n",
    "import imageio\n",
    "\n",
    "def get_image_to_tensor_balanced(image_size=0):\n",
    "    ops = []\n",
    "    if image_size > 0:\n",
    "        ops.append(transforms.Resize(image_size))\n",
    "    ops.extend(\n",
    "        [transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),]\n",
    "    )\n",
    "    return transforms.Compose(ops)\n",
    "\n",
    "img = imageio.imread(\"/data/yangchen/ICL-NUIM/Di-Fusion_demo/rgb/186.png\")[..., :3]\n",
    "# plt.imshow(img)\n",
    "regu = get_image_to_tensor_balanced()\n",
    "img = regu(img).unsqueeze(0) # NCHW\n",
    "# feat = encoder(img)\n",
    "# regu(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "pic should be PIL Image or ndarray. Got <class 'torch.Tensor'>",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_1224816/664765341.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mtimg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m255.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mregu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/.conda/envs/dif/lib/python3.7/site-packages/torchvision/transforms/transforms.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, img)\u001b[0m\n\u001b[1;32m     65\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mt\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransforms\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 67\u001b[0;31m             \u001b[0mimg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     68\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     69\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/dif/lib/python3.7/site-packages/torchvision/transforms/transforms.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, pic)\u001b[0m\n\u001b[1;32m    102\u001b[0m             \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mConverted\u001b[0m \u001b[0mimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    103\u001b[0m         \"\"\"\n\u001b[0;32m--> 104\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpic\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    105\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    106\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__repr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/dif/lib/python3.7/site-packages/torchvision/transforms/functional.py\u001b[0m in \u001b[0;36mto_tensor\u001b[0;34m(pic)\u001b[0m\n\u001b[1;32m     62\u001b[0m     \"\"\"\n\u001b[1;32m     63\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mF_pil\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_is_pil_image\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpic\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_is_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpic\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 64\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'pic should be PIL Image or ndarray. Got {}'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpic\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     65\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0m_is_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpic\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0m_is_numpy_image\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpic\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: pic should be PIL Image or ndarray. Got <class 'torch.Tensor'>"
     ]
    }
   ],
   "source": [
    "timg = torch.from_numpy(img)/255.\n",
    "regu(timg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "uv = torch.randint(0,480,(1,20,2)) # x ,y , w, h\n",
    "u = torch.linspace(0,640,10)\n",
    "v = torch.linspace(0,480,10)\n",
    "uv = torch.stack((u,v)).permute(1,0).unsqueeze(0)\n",
    "image_shape = torch.empty(2)\n",
    "image_shape[0] = img.shape[-1]\n",
    "image_shape[1] = img.shape[-2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[[[-1.0000, -1.0000]],\n",
      "\n",
      "         [[-0.7771, -0.7768]],\n",
      "\n",
      "         [[-0.5542, -0.5537]],\n",
      "\n",
      "         [[-0.3312, -0.3305]],\n",
      "\n",
      "         [[-0.1083, -0.1074]],\n",
      "\n",
      "         [[ 0.1146,  0.1158]],\n",
      "\n",
      "         [[ 0.3375,  0.3389]],\n",
      "\n",
      "         [[ 0.5604,  0.5621]],\n",
      "\n",
      "         [[ 0.7834,  0.7852]],\n",
      "\n",
      "         [[ 1.0063,  1.0084]]]])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "tensor([[[0.3940, 0.4259, 0.2649,  ..., 0.3183, 0.3812, 1.1468],\n",
       "         [0.1869, 0.2258, 0.2226,  ..., 0.1563, 0.2044, 1.5896],\n",
       "         [0.5156, 0.2797, 0.2081,  ..., 0.4193, 0.5474, 1.0227],\n",
       "         ...,\n",
       "         [0.0176, 0.1502, 0.2507,  ..., 0.1334, 0.4548, 0.5536],\n",
       "         [0.5306, 0.6953, 0.4108,  ..., 0.0097, 0.1251, 0.0000],\n",
       "         [0.0685, 0.2900, 0.2034,  ..., 0.0168, 0.0805, 0.5251]]],\n",
       "       grad_fn=<SelectBackward>)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoder.index(uv,image_size=image_shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([2.0063, 2.0084])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoder.latent_scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "256"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoder.latent_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([640., 480.])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_shape = torch.empty(2)\n",
    "image_shape[0] = img.shape[-1]\n",
    "image_shape[1] = img.shape[-2]\n",
    "image_shape.shape\n",
    "image_shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([640., 480.])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Array([[[119, 118, 110],\n",
       "        [125, 118, 107],\n",
       "        [123, 120, 111],\n",
       "        ...,\n",
       "        [125, 124, 128],\n",
       "        [125, 121, 130],\n",
       "        [124, 121, 130]],\n",
       "\n",
       "       [[123, 118, 106],\n",
       "        [123, 118, 106],\n",
       "        [123, 118, 104],\n",
       "        ...,\n",
       "        [122, 123, 129],\n",
       "        [119, 125, 125],\n",
       "        [119, 125, 125]],\n",
       "\n",
       "       [[123, 120, 108],\n",
       "        [125, 117, 110],\n",
       "        [125, 117, 110],\n",
       "        ...,\n",
       "        [123, 123, 130],\n",
       "        [121, 123, 127],\n",
       "        [121, 123, 127]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[ 50,  28,   7],\n",
       "        [ 51,  30,   8],\n",
       "        [ 52,  32,  12],\n",
       "        ...,\n",
       "        [ 81,  57,  49],\n",
       "        [ 80,  55,  48],\n",
       "        [ 80,  56,  48]],\n",
       "\n",
       "       [[ 47,  29,  11],\n",
       "        [ 48,  30,  11],\n",
       "        [ 50,  30,  13],\n",
       "        ...,\n",
       "        [ 80,  57,  51],\n",
       "        [ 81,  58,  51],\n",
       "        [ 81,  57,  51]],\n",
       "\n",
       "       [[ 51,  30,  13],\n",
       "        [ 51,  31,  13],\n",
       "        [ 48,  31,  10],\n",
       "        ...,\n",
       "        [ 78,  53,  47],\n",
       "        [ 81,  57,  46],\n",
       "        [ 83,  59,  48]]], dtype=uint8)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imageio.imread(\"/data/yangchen/ICL-NUIM/Di-Fusion_demo/rgb/186.png\")[..., :3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[119, 118, 110],\n",
       "        [125, 118, 107],\n",
       "        [123, 120, 111],\n",
       "        ...,\n",
       "        [125, 124, 128],\n",
       "        [125, 121, 130],\n",
       "        [124, 121, 130]],\n",
       "\n",
       "       [[123, 118, 106],\n",
       "        [123, 118, 106],\n",
       "        [123, 118, 104],\n",
       "        ...,\n",
       "        [122, 123, 129],\n",
       "        [119, 125, 125],\n",
       "        [119, 125, 125]],\n",
       "\n",
       "       [[123, 120, 108],\n",
       "        [125, 117, 110],\n",
       "        [125, 117, 110],\n",
       "        ...,\n",
       "        [123, 123, 130],\n",
       "        [121, 123, 127],\n",
       "        [121, 123, 127]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[ 50,  28,   7],\n",
       "        [ 51,  30,   8],\n",
       "        [ 52,  32,  12],\n",
       "        ...,\n",
       "        [ 81,  57,  49],\n",
       "        [ 80,  55,  48],\n",
       "        [ 80,  56,  48]],\n",
       "\n",
       "       [[ 47,  29,  11],\n",
       "        [ 48,  30,  11],\n",
       "        [ 50,  30,  13],\n",
       "        ...,\n",
       "        [ 80,  57,  51],\n",
       "        [ 81,  58,  51],\n",
       "        [ 81,  57,  51]],\n",
       "\n",
       "       [[ 51,  30,  13],\n",
       "        [ 51,  31,  13],\n",
       "        [ 48,  31,  10],\n",
       "        ...,\n",
       "        [ 78,  53,  47],\n",
       "        [ 81,  57,  46],\n",
       "        [ 83,  59,  48]]], dtype=uint8)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "im = cv2.imread(\"/data/yangchen/ICL-NUIM/Di-Fusion_demo/rgb/186.png\")\n",
    "rgb_data = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)\n",
    "rgb_data"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "afc46cdbaec78713ab9233e91f9bf86eb4111f2df6c8364fa345446d20f0faae"
  },
  "kernelspec": {
   "display_name": "Python 3.7.11 64-bit ('dif': conda)",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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